Abstract 242: Improving whole transcriptome library preparation workflow and data quality for oncology-relevant samples and applications
Authors: Travis Sanders, David Gelagay, Deelan Doolabh, Lee French, Jennifer Pavlica, Julie Walker, Clara Ross, Kailee Reed, Thomas Harrison, Ross Wadsworth, Eric van der Walt, Brian Kudlow
Journal: Cancer Research (2023)

Abstract mRNA sequencing via poly(A) selection is a widely used and highly useful tool for molecular profiling but has limitations in the oncology space. The template damage of RNA extracted from formalin-fixed, paraffin-embedded (FFPE) material results in 5’ information loss. The hemoglobin mRNA content of blood-derived RNA necessitates a separate globin depletion step. Additionally, poly(A) enrichment results in a loss of a substantial proportion of long non-coding RNAs (lncRNAs), which are of growing interest as cancer biomarkers and therapeutic targets. Total RNA sequencing, where highly abundant, uninformative RNAs are specifically depleted, is more appropriate for these samples and applications, but traditional approaches are both labor- and time-intensive. To address this need, we developed a simple, rapid total RNA sequencing library preparation solution that improves data quality for the clinical and translational oncology space.We improved upon existing RNA depletion methods by modifying the chemistry of probe hybridization to enable a simplified workflow while also reducing unintended damage to non-targeted RNA. A reverse transcriptase was specifically engineered to improve the conversion of RNA to cDNA - a traditional bottleneck for RNA library preparation complexity. A novel FFPE decrosslinking step was implemented that may increase the amount of RNA available for downstream processing. Cleanup steps were minimized to reduce handling time and sample loss, and enzymatic steps were combined and shortened to simplify the overall workflow. Performance was assessed with RNA extracted from whole blood and multiple independent FFPE blocks of varying qualities as measured by DV200. Results highlight improved sequencing economy and increased gene detection sensitivity with low input and degraded samples. Highly concordant gene expression profiles were observed across a wide range of RNA input amounts, and data show excellent correlation between matched fresh frozen and FFPE samples. Lastly, gene fusions were accurately and confidently identified from an FFPE fusion control sample utilizing a whole transcriptome, non-targeted, approach.This work demonstrates the utility of a total RNA sequencing approach for oncology-relevant sample types, as well as the ability to improve on both workflow and data quality in a single library preparation solution. Citation Format: Travis Sanders, David Gelagay, Deelan Doolabh, Lee French, Jennifer Pavlica, Julie Walker, Clara Ross, Kailee Reed, Thomas Harrison, Ross Wadsworth, Eric van der Walt, Brian Kudlow. Improving whole transcriptome library preparation workflow and data quality for oncology-relevant samples and applications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 242.